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Record W2082931578 · doi:10.1134/s156009041466004x

Effect of long chain branching on the properties of polyethylene synthesized via metallocene catalysis

2014· article· en· W2082931578 on OpenAlexaff
Mohammad Anwar Parvez, Mostafizur Rahaman, João B. P. Soares, Ibnelwaleed A. Hussein, Mohammed Suleiman

Bibliographic record

VenuePolymer Science Series B · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBranching (polymer chemistry)PolyethylenePolymerizationMetalloceneMaterials sciencePolymerPolymer chemistryMolar mass distributionRheologyNucleationPost-metallocene catalystCrystallizationChemical engineeringChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

In this research, the effect of long chain branching (LCB) and polymerization conditions on thermal, mechanical, and rheological properties of polyethylene synthesized via a metallocene polymerization was studied. The LCB was varied in the range of 0.64–1.14 per 10 4 atoms of C. 13 C NMR spectra showed that the distributions of both short as well as long chain branches in the polymer backbone chain are influenced by polymerization conditions. The increase in ethylene pressure leads to rise in polymer yield, catalyst activity, molecular weight, and narrowing of molecular weight distribution. In contrast, the increase of polymerization duration results in broadening of MWD and a decrease in catalyst activity. In addition, the influence of frequency and LCB on dynamic shear and extensional melt rheology has been reported. The polymer crystallization was discussed in light of Avrami model and modified Hoffman-Lauritzen theory. LCB promoted the transport of chain segments but retarded the nucleation in polyethylene crystallization. The tensile strength decreased with the increase in LCB content. The different macroscopic properties were correlated to LCB content.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.212
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2014
Admission routes1
Has abstractyes

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